Selected article for: "cluster algorithm and haplotype relative abundance"

Author: Jiao Chen; Jiayu Shang; Jianrong Wang; Yanni Sun
Title: A binning tool to reconstruct viral haplotypes from assembled contigs
  • Document date: 2019_7_16
  • ID: 2basllfv_23
    Snippet: Step 2: contig clustering based on relative abundance distribution Let the number of haplotypes estimated by step 1 be N . The problem can be defined as: given contigs C 0 , C 1 , ..., Cn assembled from viral quasispecies sequencing data, cluster the contigs into N groups so that each group contains contigs originating from the same haplotype. The relative haplotype abundance will be computed during the clustering process. The clustering algorith.....
    Document: Step 2: contig clustering based on relative abundance distribution Let the number of haplotypes estimated by step 1 be N . The problem can be defined as: given contigs C 0 , C 1 , ..., Cn assembled from viral quasispecies sequencing data, cluster the contigs into N groups so that each group contains contigs originating from the same haplotype. The relative haplotype abundance will be computed during the clustering process. The clustering algorithm we adopt is prototype-based clustering and is essentially an augmented K-means algorithm. In a standard K-means algorithm, the centroid of the objects in a cluster is the prototype of the cluster. In our algorithm, the prototype is a distribution that is derived from the contigs and empirically describes the relative abundance distribution.

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